Relevance feedback for real-world human action retrieval

Simon Jones, Ling Shao, Jianguo Zhang, Yan Liu

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)


Content-based video retrieval is an increasingly popular research field, in large part due to the quickly growing catalogue of multimedia data to be found online. Even though a large portion of this data concerns humans, however, retrieval of human actions has received relatively little attention. Presented in this paper is a video retrieval system that can be used to perform a content-based query on a large database of videos very efficiently. Furthermore, it is shown that by using ABRS-SVM, a technique for incorporating Relevance feedback (RF) on the search results, it is possible to quickly achieve useful results even when dealing with very complex human action queries, such as in Hollywood movies.
Original languageEnglish
Pages (from-to)446-452
JournalPattern Recognition Letters
Issue number4
Publication statusPublished - Mar 2012


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